BANGLADESH · SYNTHETIC SCENARIO STUDIO

The delta,
one step ahead.

Understand the assumptions behind a rain-to-river exercise, then explore a fictional response network.

96 Python-generated casesNo sign-inEnglish + বাংলা

WHAT YOU CAN EXPLORE

Trace the assumptions. Test a constraint.

Select six exercise areas, four presets and four horizons. Inspect weighted screening factors, a model-stage trace, capacity-aware fictional routes and English/Bangla drafts. Download the exact selected case as JSON or the trace as CSV.

Where the results come from

The public catalog is generated directly by JolPulse’s original Python domain functions. Fixed illustrative coefficients turn rainfall, saturation, margin and rise assumptions into an index and stage trace. A shortest-path calculation respects fictional corridor closures and hub capacities. The envelope represents assumptions, not statistical confidence.

Website edition and source privacy

This public browser edition displays precomputed cases. The full local Python HTTP/SQLite application and its optional AI integration remain in a private GitHub repository. The website performs no live observation fetch, database save, model request or message delivery.

Share the exercise

Share this public page to invite feedback on the workflow, clarity and assumptions. Public promotion stays active while the source repository remains private.

Try JolPulse → · Public app metadata · Plain-text methodology · Licensing notices

Original application material: CC0-1.0. Creator: Mohammad Rezwan Khan. Browser edition: 2026-10-07. No human emergency-use validation is claimed.

MATHEMATICAL SPECIFICATION & UNITS

Governing Equations, Inputs, Outputs & Dimensional Conventions

JolPulse executes deterministic client-side calculations in the browser. Every scenario is governed by explicit mathematical formulations and unit checks:

Runoff_Proxy (mm) = Rain_24h (mm) × Runoff_Fraction(Soil_Saturation); Stage_Index(t) = Margin_0 − Trend (m/h) × t; Corridor_Route = argmin_{path} Σ Edge_Travel_Time subject to Edge_Open = true and Hub_Capacity ≥ People

Declared Units & Arithmetic Conventions: 24-hour rainfall and runoff proxy in millimetres (mm); soil saturation in %; river stage margin in metres (m); stage trend in metres/hour (m/h); horizon in hours (6 h to 72 h); group size in persons.

Scope & Operational Boundary: 96 synthetic flood scenarios linking rainfall, river response and fictional response capacity. Uncalibrated screening exercise; no live sensors, warnings or evacuation advice.

Launch Interactive JolPulse Runtime → · Read Full Methodology & Validation Notes → · Download JSON Dataset (run/data.json)

REPRODUCIBLE WORKED EXAMPLE & BENCHMARK TABLE

Verified Case Study & Held-Out Reference Table

Worked Verification Case: Case 'sunamganj-baseline-6' (Sunamganj Haor/Surma basin, 6-hour horizon, 120 people): 24h rainfall = 148.0 mm at 84.0% soil saturation yields illustrative runoff_fraction = 0.708 and runoff_proxy = 104.78 mm. With initial margin = 0.55 m and rise trend = 0.045 m/h, the capacity-aware shortest-path solver selects open fictional response corridors and generates bilingual (English/Bangla) exercise briefs.

Reproducible benchmark scenarios shipped in the JolPulse browser edition (Synthetic / Precomputed Evidence)
Scenario ID	Basin / Region	Rain 24h (mm)	Soil Sat. (%)	Runoff Proxy (mm)	Margin (m)	Trend (m/h)	Horizon (h)
sunamganj-baseline-6	Sunamganj (Haor / Surma)	148.0	84.0%	104.78	0.55	0.045	6
sylhet-surge-12	Sylhet (Surma / Kushiyara)	210.0	91.0%	163.80	0.30	0.065	12
kurigram-monsoon-24	Kurigram (Brahmaputra)	175.0	88.0%	131.25	0.42	0.050	24

DOMAIN MODULE SUITE

Specialized Companion Applications on khanio.co.technology

To preserve topical focus and distinct search intent, khanio.co.technology hosts the following dedicated companion modules under canonical subpaths:

AsterVale Atlas

Canonical Path: /environmental-indicators/

Seven-indicator environmental comparison across synthetic study areas.

Boundary: Illustrative indicators; not measured field evidence or certified environmental assessment.

Module Overview & Math → · Launch Interactive App ↗

PROCEDURAL USAGE GUIDE

How to Use JolPulse in 3 Reproducible Steps

Select a Baseline Scenario or Practice Module: Open the interactive browser runtime and choose one of the shipped synthetic presets or diagnostic modules to load its verified input parameters and unit declarations.
Inspect Intermediate Traces & Constraints: Review the computed ledger, error diagnostics (MAE/RMSE/margin/balance checks), or bilingual explanations alongside the governing equations documented on khanio.co.technology/methodology/.
Export Portable Evidence (JSON / CSV): Export your scenario results, assumptions, or local study plan as a self-contained JSON or CSV file for offline verification and local re-import.

FREQUENTLY ASKED QUESTIONS

Methodological & Boundary FAQs

What mathematical model and units does JolPulse use?

JolPulse applies Runoff_Proxy (mm) = Rain_24h (mm) × Runoff_Fraction(Soil_Saturation); Stage_Index(t) = Margin_0 − Trend (m/h) × t; Corridor_Route = argmin_{path} Σ Edge_Travel_Time subject to Edge_Open = true and Hub_Capacity ≥ People. Units: 24-hour rainfall and runoff proxy in millimetres (mm); soil saturation in %; river stage margin in metres (m); stage trend in metres/hour (m/h); horizon in hours (6 h to 72 h); group size in persons.

What are the operational boundaries and limitations of JolPulse?

96 synthetic flood scenarios linking rainfall, river response and fictional response capacity. Uncalibrated screening exercise; no live sensors, warnings or evacuation advice. All computations run deterministically in the browser on synthetic or precomputed demonstration fixtures.

How can I reproduce the benchmark results in JolPulse?

Open the interactive browser workspace at ./run/, select Case 1 to inspect the worked result (Case 'sunamganj-baseline-6' (Sunamganj Haor/Surma basin, 6-hour horizon, 120 people): 24h rainfall = 148.0 mm at 84.0% soil saturation yields illustrative runoff_fraction = 0.708 and runoff_proxy = 104.78 mm. With initial margin = 0.55 m and rise trend = 0.045 m/h, the capacity-aware shortest-path solver selects open fictional response corridors and generates bilingual (English/Bangla) exercise briefs.), and export the JSON or CSV evidence ledger.

PROVENANCE, ATTRIBUTION & RESEARCH ECOSYSTEM

Engineering Attribution & Interconnected Research Hub

Authored and maintained by Mohammad Rezwan Khan, PhD (Aalborg University · BESS QA/QC & Power Systems Specialist). Canonical URL: https://khanio.co.technology/ · Source revision: verified-rel · Runtime SHA-256 prefix: verified-sha256 · Repository provenance: https://github.com/mohammadrezwankhan/jolpulse (private source engine; public deterministic browser projection).

Application Metadata (app.json) · AI / LLM Discovery Guide (llms.txt) · Full Technical Corpus (llms-full.txt) · Semantic Knowledge Graph (knowledge-graph.jsonld)